Recommendation Engine Market Trends, Drivers, Strategies, Applications and Competitive Landscape 2022

Recommendation Engine Market

The Recommendation Engine Market based on AI, is expected to grow from USD 801.1 million in 2017 to USD 4,414.8 million by 2022, at a Compound Annual Growth Rate (CAGR) of 40.7% during the forecast period (2017–2022). The recommendation engine software and solutions based on AI, help analyze a large volume of data, such as purchase history, customer profile, and demographic data, to understand customers’ likings and preferences. The major driving factors for the recommendation engine market are increase in focus toward enhancing the customer experience and rise in the rate of digitalization.

The Asia Pacific (APAC) region is expected to grow at the highest CAGR during the forecast period in the global recommendation engine market based on AI. The growing digitalization rate across various industries in the APAC region has led organizations to adopt an efficient recommendation solution to analyze large volume of customer data to design recommendations. The vendors in the recommendation engine market have focused on delivering AI powered recommendation software and solutions to fit organizations’ product/content personalization requirements.

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The recommendation engine market by type is broadly classified into collaborative filtering, content-based filtering, and hybrid recommendation. The hybrid recommendation type is expected to grow at the fastest rate during the forecast period. As the end-users in various industries, such as media and entertainment, retail, and transportation, which are focused on deploying AI-powered recommendation engines with hybrid recommendation data filtering type to create accurate recommendations. Furthermore, the hybrid recommendation type combines 2 data filtering types, the recommendations created using hybrid recommendation have proved to be more useful, and therefore this data filtering type is expected to grow at the fastest rate.

The recommendation engine market by deployment mode is segmented into cloud and on-premises. The cloud segment is expected to grow at a higher CAGR during the forecast period. The cloud-based solutions offer ease of deployment, reduced operational cost, and higher scalability. The recommendation engine solution based on AI, deployed in the cloud offers several benefits, such as easy deployment of real-time analytics solutions, security, and flexibility to companies.

The recommendation engine market by technology consists of context aware and geospatial aware. The geospatial aware technology is expected to grow at a higher CAGR during the forecast period, while the geospatial systems have gained traction as these systems provide various recommendations, such as place recommendations, within a specific geographical range.

The recommendation engine market is segmented based on applications into personalized campaigns and customer discovery, product planning, strategy and operations planning, and proactive asset management. The personalized campaigns and customer discovery segment is expected to have the highest market size during the forecast period. This is attributed to the need for automating the traditional process of customer segmentation and targeted campaigns, and growing requirement to analyze the customer data, which have fueled the use of AI-based recommendation engines in applications, such as personalized campaigns and customer discovery.

Furthermore, the recommendation engine market has been segmented based on end-users into Banking, Financial Services, and Insurance (BFSI); healthcare; retail; transportation; media and entertainment; and others (telecom, energy and utilities, education, and manufacturing). The media and entertainment end-user is expected to grow at the fastest rate during the forecast period. This end-user has deployed recommendation engine software and solutions, to help its customers find relevant content and products from numerous catalogs. For instance, end-users, such as media and entertainment, and retail, have deployed recommendation engines based on AI, to provide an easy content searching experience to its customers.

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